AI feels like magic, but it's built from a few understandable parts.
Direct answer
Most modern AI works by learning patterns from huge amounts of data. During training, a model (a large mathematical system) is adjusted until it predicts well. After training, it uses those patterns to produce answers for new inputs, such as text, images or recommendations. AI products wrap that model in an app with instructions, tools and checks.
The layers
| Layer | What it is |
|---|---|
| Chips and computers | The hardware that does the huge calculations |
| Data | Text, images and other examples the model learns from |
| Model | The trained system that makes predictions |
| Agents and tools | Programs that let a model use search, code and other tools |
| Apps | The product people use |
| Evaluation | Tests that check quality and safety |
An example: predicting words
A chatbot model is trained to predict the next word in text. After seeing a vast amount of writing, it gets good at producing sensible continuations. When you ask a question, it generates an answer one piece at a time. See how large language models work.
Training vs using
- Training: slow, expensive, done by AI companies.
- Using (inference): fast, what happens when you chat.
Why it makes mistakes
It predicts what is likely, not what is verified. That's why you should check important answers.
Beyond chatbots
AI also powers recommendations, image recognition, translation and fraud detection. Agents add the ability to take actions. See what is an AI agent.
Where Teyro fits
Teyro's The AI Stack course teaches how AI products are really built, layer by layer, and you build your own AI assistant in your browser. It has 100 short lessons and is free to start. New to AI tools? Try AI Tools for Everyday Productivity.
The bottom line
AI is patterns learned from data, packaged into products. Knowing the layers helps you use it wisely.
